{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch.autograd import Variable\n",
    "import torch.nn as nn\n",
    "import torch.nn.init as init\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "import numpy as np\n",
    "from six.moves import cPickle as pickle\n",
    "from six.moves import range\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train set (97722, 1, 64, 64) (97722, 6)\n"
     ]
    }
   ],
   "source": [
    "pickle_file = 'SVHN_1x64x64_train.pickle'\n",
    "\n",
    "with open(pickle_file, 'rb') as f:\n",
    "    save = pickle.load(f)\n",
    "    train_dataset = save['train_dataset']\n",
    "    train_labels = save['train_labels']\n",
    "    del save  # hint to help gc free up memory\n",
    "    print('train set', train_dataset.shape, train_labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "valid set (5679, 1, 64, 64) (5679, 6)\n"
     ]
    }
   ],
   "source": [
    "pickle_file = 'SVHN_1x64x64_valid.pickle'\n",
    "\n",
    "with open(pickle_file, 'rb') as f:\n",
    "    save = pickle.load(f)\n",
    "    valid_dataset = save['valid_dataset']\n",
    "    valid_labels = save['valid_labels']\n",
    "    del save  # hint to help gc free up memory\n",
    "    print('valid set', valid_dataset.shape, valid_labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6227\n"
     ]
    }
   ],
   "source": [
    "c4 = []\n",
    "\n",
    "for i in range(97722):\n",
    "    categ = train_labels[i][0]\n",
    "    if(categ == 4):\n",
    "        c4.append(i)\n",
    "\n",
    "print(len(c4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "159\n"
     ]
    }
   ],
   "source": [
    "c4_val = []\n",
    "\n",
    "for j in range(5679):\n",
    "    categ = valid_labels[j][0]\n",
    "    if(categ == 4):\n",
    "        c4_val.append(j)\n",
    "\n",
    "print(len(c4_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6227, 1, 64, 64)\n",
      "(6227, 6)\n"
     ]
    }
   ],
   "source": [
    "c4_data = train_dataset[c4]\n",
    "c4_target = train_labels[c4]\n",
    "print(c4_data.shape)\n",
    "print(c4_target.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(159, 1, 64, 64)\n",
      "(159, 6)\n"
     ]
    }
   ],
   "source": [
    "c4_data_val = valid_dataset[c4_val]\n",
    "c4_target_val = valid_labels[c4_val]\n",
    "print(c4_data_val.shape)\n",
    "print(c4_target_val.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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W/vvu5Ti7uxZhYY/WYpE8C5/71lpscD9bi43rb1/J9+3LMfJ4VT1Yi0XtxRuU64drsYH9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IAAAg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\nG2HoU6o0xekZVsx5MvyKYagMv0rhcPu8aTulrTF8LFUZ47IYatpJB+N6c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WqQnD69doDz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\n/bZ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53z00cyvrdcLX56aTUCelOvUtpTpxWamST6oolI79znbYdd6kFOmU7tS5jqTty2sr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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f44c8af19e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (20.0, 20.0)\n",
    "f, ax = plt.subplots(nrows=3, ncols=5)\n",
    "\n",
    "im_samples = []\n",
    "\n",
    "for row in range(3):\n",
    "    for i, j in enumerate(np.sort(np.random.randint(0, c4_target.shape[0], size=5))):\n",
    "        im = c4_data[j].reshape((64, 64, 1))\n",
    "        house_num = ''\n",
    "        for k in np.arange(c4_target[j,0]):\n",
    "            house_num += str(c4_target[j,k+1])\n",
    "        house_num += \"--\" + str(j)\n",
    "        im_samples.extend([j])\n",
    "        ax[row, i].axis('off')\n",
    "        ax[row, i].set_title(house_num, loc='center')\n",
    "        ax[row, i].imshow(im[:,:,0], cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Tj/DXaVMC2f32q7j913DL0+goikxpYWXgfwHvKG069bnXfcTKzc92FFW8x97B6pd5mqqr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cv8qjHG6THG+8YY34zP31hVd03TxIvzb9dZesgk7ZMxxvePMZ6po3/keLCqtl0nx/F/P+PaNMYY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\nh/2d92UphZHzMJWQ15aUOpqud+xPnE7V6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EnamQ+BJEmSJEmSVuCCpYMxtGpp+HpHCu3k\nclJqRaoaltJSUkpEquSVUiVS1QbuH7azE5KWwn03/78Tksdw1ssuu2yeZqgi37jO0EZWSOI+euyx\nx+ZphsozfDelgzFUdTOd5ozNcOEzloZWp/P1Ypf65v/H3r0Ha3Lf9Z3/tm/4opt1G0m2pJElIRvZ\nlmUUFowx3opNNixeQjah4oWCLCSbSiqbrQ2VrdolEGqhNhuSVCpblSybhI0xtzKBUBtvAEMg2MZA\nbLNWUHyTZd1H0uhq62KDwe794znTfs+j53Pm23POzJxn+v2qmvJPz+nTz6+7f7/uPu3vt7+dkPIk\nVSBKqWGdlIsUjpuk6nY85uwDUx55HuAc5BhnqkSqOpKWqcohtqmv3HfsX6oCxfmSjjHnOz/n3E8V\nglI1wFTZIlUg4ufpvJlSS86WOdupOJG2Oy0zN82E0txJVeA4R3gu59hJKWAMA+fnnGspJa2Tjsp5\nzfGe0gfWf5+/w3nHSipMP+H1LqWGpus9U1H4vamqF9fJfc17C+7fVBGM+zRVC02VklIKWCcdYBtS\nTlJqSaqKxWOSUoXSOZ7LpEp5qQ88PindkPdW7BuvralaH9NJUvXUVKmV6RocXyk1LFUrW18vpXvI\nO+64Y+PnXA+rBXEe8RrHuZnmLM9ZfA0Cr4mpSi+/l8fs0KFDUztVVeR+TClsKU0xpaxvw9xM9+9p\nPu6WArzpc57DOEc6FaVT34jHin9jvelNb5ra6X4gvd6DqVS8j+Ox7VSpSmOB17prrrnmuD5x/6bf\nT/cT/LvyiiuumNrvec97pnZ65QjnIOcRr8UpJbNTHTlVxO5c19JzjZSanf622Uv658GfyZIkSZIk\nSdozHwJJkiRJkiQtwGlNB+tUMWDoWtIJiWLoJd8IzlAvhtjtVkXrRH1gaBv7z+/l5ymUu5PGkt4q\nn9LW0vLrP0vVGhh6y3A+hhIyVO+yyy7buP577713ajPUrROCzDA/hvVSCi9P4XlcTwrf7ByPba5g\nshedilpJqs6Twi1TqDLbnWPF5TlHUh84HlM4Os8nKSUtpYKmsOuq40PBmdqa0jfY5pin1I/UTucU\nhhSnaiapWl9KV0nHL33O392G0PTTKaV9dc6FaXzy+PO6+cQTT0ztlE7BY84QbM4prp/h27zOcD7y\nmsAQb35XSslJ6WYpTXV9DKbQa+4vXvuZTpKqbfL+gOcRXk8vvPDCjdvA7eexTNUzUyo3t5nnHG4X\n9zu3hfuR+72TDpba2yadO9P5KVVuTClU6ZqVzrvp+sKUDR5n4jWuky7NscnfpZTOxvMAxya/N6WH\np2td1fH35fx7gOeg++67b2pznrKvvN6xrykNhNvPdFOuJ/0uz1+cvymlnFIl4/WUuU3fm1KC0zV6\nG+ZpmiPpNRrp3iRViOJx4PiiTkW8zjWa/ecc4fmYr9PgdZnXIo4jVtDiPEipWuk+nJ+nKtVVuaJl\npwIX51H6+4HzLt3TpL6mlKs0VtLfPKn/naq1aX6llLHd/rafw7tnSZIkSZKkBfAhkCRJkiRJ0gKc\nsepgpzqcMIXDpZBdhn8yJC+lnKTKHgyvZbhWqnKT2in0LoVps8+pWtN65YT0Oym8LYWRpza3mfuX\n4cjcjwxt5LYxbC+tM4XtpZBKSqF0aT+mY7Ok1LAUrsh9kKotMDyZY5LHk22uh6kSDG3l55wjKd0y\nhZEyZJXzI1VFYR9SJY1UkYPrWQ/x5phnqHGq2NVJh+R3pHDcdJxS+h/3e0pF4HrS+TRVJqJOylsK\n6U5zfNtSyTrVM9N1IVXn4DrT/uO8YEWalALJOcX181rM302VkphCwXWma1c6F6XzUqrKlKoMVeVr\nE69fDz/88NRmJaCUwpnGPMcnU664L3g9ZVWyVGmJKWaXXnrpxmWYGkPcRn4vKzxxPZ0qJ53U322T\nqsGk802qcJj2WbrX4JxN902pn0yhSOlX7BtTNHg94T1gqhrHz1O1unT+2W1upntxjlWeg7gM27yu\n87t5zkrHOFXmYprrJZdcMrW5r9m3dM1N1RmTzvijdE+7belgad+k+51OSjrXme4/O3M2HYd0bDm/\n7rnnnql96623Tu1PfvKTUzul/3L9V1999dRmOudv/dZvTe1OmlH3nJ3Oa6mdrgucI6lKYudVCTxm\nqbp0uq9O6WBpG6mTDpb2Q6dy29xK6tt1ByxJkiRJkqST4kMgSZIkSZKkBThj6WCnGlMomBrGkLFU\npYu4fErpSlU4OhWoUjhiSj1LIW8McafOW8nXf8aQOYaqMvyXoa2pzXA7fs4qMlyG7bR/U/WXFDrb\nkVJL9vLG9W2TQmFT2gXHJNscLwyFZpvLcNymcGmGiDPdgWGhrPjBZXgeSCldPA/wu1KYJ9upQgTD\nzzlnuf4U+r6+rpQmye1nhb50jkvhr6lKDY99qkiRqiGyigr7wOVTel5KZ2KfUwWalALWSRk6qNK1\nI0nh/6myHq9TvFZy36cUD/4ux0WquMcQ+nRd4zHhcWZqWErx5vpThbKUIkf8fH0ZrospJOl8lMZ8\nmqcplS5V/+H2P/jgg1Ob6UA8t15++eVTm+eNlObJbUznuHQ+SSHuKWQ9pSNug04VsLnVM1ObcyTd\nE6bqUlyGn/PYptQVzinOx5QSkqqGpfuKlCreqay0vj08N6Vqm2xzzBPnWrr2dapJcds4H1PKVTre\n6VpJc1NCzkbpvpRjJL2yg9LY5rjgWOikPaX1p+sU04t/7dd+bWrffvvtU5vbdcUVV0xtbnuqEnjt\ntddO7d/+7d+e2hyP6e+klOK/Pjc7lQuJ60rbkPZp+nuQy6e/zzupWOm7Ote7tJ5078p9xc9T2vXc\nVxwYCSRJkiRJkrQAPgSSJEmSJElagK1JB+uEwTMUi+F56a3/DHFP6+9UY2GYW2ozZIxpIOmt/+xn\nCmdLIfQpjWU95SdVT0mhtym0NYXwMlyN6SFcnhi+ye9NVU7Sm/dTGkhKP2CfU2hiCrtN6SQpXHsb\npOpSndDLtJ40plLId6f6SZprKSQzhVjyu5jyyDHLz1NlntQ3bi/HL1NA1udmqlDI81pKk2TqFteT\nwte5X/h5Cv9lX1MFFq6T/elUbOK4Sek6qTJCmvspnW8bUk46FSeIc4q/m1K3jhw5MrXvvvvuqc10\nD1aU4rzgNTSlUqZUPLZ5bk7h1RwLHFP8nOOR257SxCilRXKeVh0/ZpgCxipgaS5wDhL3L1NoeB7h\nMWM1Lm4bq3qxn0wPYDpYSknldvH627mvohROz8/5vSll8aBK1/lOSH66Z0u/27nOplcKpH2f7olS\ntRxeE7ielPqdKrhyGfY/fd6VXt+Q0k/SNTtV5OVcSGmobPO+gea+vmAvVSy3oZLXqdBJzUn3DnMr\nWXHszL1eU7p/ufPOO6f2Aw88sHGZTtopr2u8H0ipVynNt1Mpa/2awHXx+1KqajqXdVIgO5W22J/0\nSpdTLfUt3Yen89VeXodiJJAkSZIkSdIC+BBIkiRJkiRpAbYmHawTVpfCv1OIcQorSylQDNdKIWMp\npJThZgxHT1U+2IeUGsZ1dqpIrIfBpwouKZws9TuF16e0DrY7lXpuG72qAAAgAElEQVS4T1PaSKpu\n1gmFTalESapeRJ3Upm3QqWpHu1Xu2CQdQ86FVIksvTW/UxEvhd0y1SulgzFkvROqmaqrpCppVcdX\nD+H3pW1IVXXS/koVFlIILnGbGSrPdBVuM6uucJ+m1FGmEPC7uM65aVwpbWMbKgCm1LdUlaNTxYPV\nRlhhhO3rrrtuavMaxHHLdCKO2TT+UxUVtplywSpbTFHifLn00kunNsc7w92ZnpWqJnWuaVXHj/mH\nHnpoat9zzz0bl0nX2VSVkGl4qRoil+GxZHU3psxdffXVGz9n33j8UmU1ziPuX1aWStdrSvM33W8d\nVGkMp/GT0qLTPVunSlc6P6Q0zNQfrjO9WiFVw+TYSddr9iHdG3JssopsslsadUrv5Hbyes8252/n\nGpS2OVVD5H5M+4VSmlsaH6liHHXGREob3wZpXuylalOaI52KVZ370vQaAKYad159wLGcXjOSKsFy\n/el1DZTSPHldqjr+fEGpemZqd1KPU/pYuk9KFRPnVndL9+TdV7Qcw/GUqpincTmXkUCSJEmSJEkL\n4EMgSZIkSZKkBTit6WApnCylLDAFI4V6pYpVXA9DwTthUwxHf/DBB6c2w7FTSBr7nCojsDoH+8y0\nCYb/pTe3M5wvpYYxZIyh4us/4zazryn8MYUVcpsZkpgqE6X0OUoVmxiCzP2SQm3ZzxS6zdSCVIEm\nhUhy/zBUfpvxmHAfpLmcwrFTmGSay1yGY4f7lWOwM45SKkZKS0ppWKkqWao2kNKP2AdW+6k6fuxx\nbKdwVqaTpL6mcPSUSppC9lPlPqaDcd+lyhnp3J3S7Trh6ClktxMef1B1Kg2msOV0zmaaFVOa+Pnh\nw4enNsczx20aI51KSSltjddrVi5jVZRDhw5NbZ6z+V3clrQfuF2pAuB6paRO/9L2p8pq/L6UQsX7\nA96L8LrOucZzAquP8bpJ3M4039P1LqXApHNo2j88F6fxcVB1qnrNrTLaScHuSClmvBdLqQmpClKa\nRyndklJFMF4HTibVnr/DdXF+cZ7zOsvrF7ch3TemFK1UWS2l8qZl0vHoVM2bm/LUqb60bTpV87hv\nOumZ/Jz3op20uc46U0pfeiVISklLVTWJ1/H77rtvajMVMv3dk9KbuE7+7bzep056F78jVSlL6+9U\nF2ZfuU5eZ9NYSf3spIyleZ1eaUIpjTr9/dthJJAkSZIkSdIC+BBIkiRJkiRpAc5YvG0K10ohc+nt\n5WwzJIohnyntIFWhIYaGMfyP4V1cT6oixH4yfJ0h2ykcleFy3JYUKsz+pFDAqpzqwza3IaW1pPDc\n9Lvp2DNMjutnyC7TZlI6GI8Z9zuPcdpeHg8eS66/U0mts70HSaeiWvq8sz9SKDvDPFP1PaYcMRUj\npYN1+sljktLcUnofP2ffGEab+sM+cKxdcsklx/WVqSKpikGa82mspv2b5i+/N1U1S1XJOiHoKfWj\nk1bR+Tx9b6dvB0mnQgWlMGrOF7bTmOT5NaXlpcobxHHBtIxUAZPpTUePHp3arBbEvqVrIqUUYWKq\nFvfDenoHU7WZSsdzAa99PL+k6oMpbSqllqQU+pRaxWsZK4ulqigpPZPHspPeQJ1KjdsmnZs792md\ncdtJG0nr7KyHn6drVkp14njh9ZHt9BoAStuS5gTn0PoyqdInl+O8YztdNztVkVIfKN0zp2s0pTnV\nSVHpmFtBbBuktKlOOlF6XQSPW7r/pE4aUKcPHL/pHi2lnbJ9yy23TG2ma73vfe/b2B/OCV5zOb7Y\nB/bzzjvvPG5dKW0qpZKm15qk10ak+7p0n5HuIfj3Y0r5nDsv0nW5UwmS255ea7CXSppGAkmSJEmS\nJC2AD4EkSZIkSZIW4Iylg82tkkAp1JHrvPDCC6c2069SikMnDS1VDGBYGT9nCDaxbwxHZfhqets8\nU064DMPXuU6mT/F71/vH72AIYAr9ZyhaCoNnWB1x/6YQQ4asp5BEpomlUMgUepfC+dIb7CmlxdE2\nh7in8GxKKQidMOeUzslQzaeeempqsxIO2xx3KZQ9heh30o+IfUtV9Tg30/mN44Lzb706GP+7kw6W\nKjR0KiakdDUun9LBUtWVVO0opR3ODbudmwJGe6mycyZ0zkmd/cF9zzHC82iqlMhj20kdTXM/jWW2\nmU7C+c5UUM47bmOqPJmqB/J6xTHL/bCeksVzAec8z1mpShf7x/VyPfyc60xpmOmckOYyjz2/qzP3\nU9UZ7sd0XkpzubPMQdVJfeucvzvLpGOS7mlTSiKlNBPOEY5ZtjkPUpVI3sd1UsPS9ZH7Ybd0MP4+\n52BKl06pYdyGzt8MnXsm6qQ8p3No+t2USrQNryM4FdLfLiktKb3CgeOF0v0nzU3fTmnarCjdqRjI\n60ZKO77qqqs29pPrT3+H8bqRXoHy8MMPH9enlOKU0sHSfWy6/5j7ugq+coG/m9LB0t+/nb8B+bvs\nf3oNTRoTXJ7r5PGYO9+3625YkiRJkiRJJ8WHQJIkSZIkSQtwWtPBGHKV0nQoVeqhTmUi4jIMs2Ll\nEa7zyJEjU5thYgwHY3jqoUOHpjbD6RlKd/XVV09thtKlkK5OGlbn7f7roWcphJt95XFiyCND8xmy\nz+PEEHeG+XEZhioy/I/pMPycIXxMDUshkqm6W6pyksZlOgaU0pC2IcS9E944N5w/pYBx3nG/pjnI\nsfboo49O7ZR+wWObwqhTaGoKo05v6Oe2cKyluZxC+hl2u/7fXI79S9UvGPrPfvDYpM95Tkj7guvn\nfOQ+YopRqtTIY5YqynCfpnD3dG3oVKrbhrmZUicozcGU7pTSannu5+f83fRdqbJPOlbcrpSu0qm+\nx+9K592UntlJh1nHn3F88nzE8ZyqrFGqqsm0lHQ/lPZpp3JbmoMpJTOdEzph7ek8m2zD3NxL2vfc\nVx/MrbrGOcV7qHQM03jh3E/rT+vhOOJ5ndefVAEvpcKl9vp/p1czcD6yEmGqDsh9x7mZUsMopWyk\n+buXKj970Ukx2jbpXildszpVmHh97Pw9kSqmpvtk3k/efvvtU/vKK6+c2pdddtnUvueeezZ+L8c1\nr+O33nrr1ObfqjfeeOPUvuuuuzb+LrHPV1xxxcblOT+qcqob9xH/luQ85X1AOq/xd1NlwfR3OI93\n5zqYronpc7bvuOOOqX3ttddObZ5/ePxSKlnaxs79Ip0ds12SJEmSJEm78iGQJEmSJEnSApzWdLBO\n6OzcENkU2ppCNRlaxfDyVA2EIWYMZ+V3MXWJKWB8+zpDt/imd4aA8XsZ0vXII49sbKeqDVzPbpWs\nUiUChswx9I4hgKmCCTGUnVKVmosvvnhjm/1h+gn3e0oHYopRSjlIKWApRLZTZWzbdNJrUkWtFNqa\nKvVwfzOd4qGHHtr4OccXl+H4SqG5Cccv53U6zgxl5fbyd3k+YfgqpfD+9UqCqSoD9x3b3GaGkrJ/\n3I9chtvD81EKQ+XynKdcPqUScRykY8w292MKre5UYUiVVnZL+zko0jk8XQdTRbiUKpGqt6V0j5TK\nkSp4dPZ9SoeilH6U0lhSRTDiHOL2cn+up6swPTWlqKX9xfNUunbwmshl1sPrT4T94TxiH1JaN5fv\nVHJNaXVzbVtVTfY3VexK959pPamd5hGlOZ7SmzhGOE55zWGb52b2jef7dJ+V0l54jUrpY50qaev/\nnbafVXI5/nk/yf4xLSVdlymlvnPepcpS6VyW7ks786WTFpru57ZtPtLcCk7pd9P1l8eTy3DcpVcT\ncP1pmXe/+91T+5Zbbpna11xzzdTm/TDnC/vGv4F4XuL9M+/RDh8+PLXTtZ7zgP1J1UV36wfHHiuK\n8d6S96ucCzfccMPU5r7jnE2vH+H6UypgOh9xeZ4HOim7v/u7v7uxb6973eumNq/F6bUUqero3JRS\nI4EkSZIkSZIWwIdAkiRJkiRJC3Ba08HmVjegVH0ghYgzHIwhYAzpSlU+GMLJcDCG+XFbGArLdCV+\nzn6yP1wmVUpK4eHcFoakpXDZ9ZST9f8+5pJLLpnaTD9LIWopfC6FEvJ4p/A8vgGf28BlGL7L8Foe\nS4bMMZSZ/UnHldLnqWIRx+KZqvhwuqQUD+6zFFbJdAqGtqbUBI5HHs9UcYx94DHh8gyLTSldqTIA\n+5nSRBiqmSpfraecpDnMsF3OeW7PAw88sLFPDKll2C3xWKZwZ+7HdG7l+Zfzi9vM/c7zHT/n8jS3\ncuTZIoW4p5STFNbeqf6UqslxXKRKGp0qWJRSHLg8xzvTjzhP2U/OCbZTCk+qoLSe2sk5z7HK32Ff\n0/HopCiktJwUTp+qn7KfbPOcwHanKl+3YtOm3+18vg1SZbqURp/SDjo64yil9TMFiqkcnL+pqmqq\nMNhJvU3VsVKKUro+0m73tGk57gtuA+8h0zWI95D83XSMuU/ZB66T9zEpDY/7NF3v0jUupYd0UqrT\nayLS9eOgSveB3NZU1Sudw1I1xfS96ZxA6f7lsccem9q8X+M6eZ9JXIa/y7HGezSOO473VBmNKWNp\nnrGiWVXVJz/5yamdjg3nP/8eSK9r4XF62cteNrXT+ej666+f2pzLvManVM30momUvp7GAdfJY5y+\ni9ue1jm38iadfXfJkiRJkiRJehYfAkmSJEmSJC3AGUsH64Tzpzfip3WyzTQFhnwyJJPhV500Jq6f\noX0Mr2U6GD/n8gxhZZshXSlFKVUU4fIphJxhflU5XY1vdX/00UenNkP1qPN5ShVgSCLT0A4dOrSx\nbyndjtvPcP20H1MaYScEN43XlA7WqVZ1kKQQ4zT2UjoY9wfHApdnKGWqHsCQV6Y3pXWmfnKdaWzy\n/NCplsNwTvY/VWrgWODc4netf1+qwpMqf7HN/cXvYzh6qiiTzn3cjzx/sT9MrUlh0NzGlJKXzi2p\nUk4K4+5U1jmoUopWuiYyfDhVdOR+ZdoQxxfnBccUP0/VsdLx5zUopcymSnypCgfHMpdnnznv0vk+\nVRxbD/tPaajcTu5H9jWldnL7UwVALp+WSedcnpu4H3n+SpVQ03UtVZVLqRc8Np1rzDZU3kzpXZxr\nad8kHDtcZzoXpjnC7+W9KFPDOO54Ludc5u/yPJ1SHFJ6CO/1eJ7mdqVrAr9rt+pgqZoe+8G/DXjP\nyX2d7t15z8n1pLmQ0tvSPQH7kF5XkaqupvmV/jZgP9PfPCn1Zhukc0nntSTpb67drgubluHfnvyb\nJqWkpb91eN3k73LOphR/jkFeK3nt5njnd3F+MN2Kr+tIr2e59tprj+vHhz/84Y19TfcrvF/lMtyn\nPFem+yH2lWlsvJ7yPpntdK/IcyjnVErp2q0S8DFHjx6d2qnaHLc3VWY1HUySJEmSJEnP4kMgSZIk\nSZKkBTit6WCdcMLOG9o7yzN8LH2eUkIYepkqFDB8ju3LL798ar/85S+f2gyPT9UWUgpISrchhoZx\nW9Kb/quO3xcMOWOf2O6kovBzthkCl1LPuO/4OcPtuB72J4U1p4oyPK4pbDiNobnVGbZBJ12GYYap\nukcKZ06hiykVi1I1sU6KXkoTS6lkDAVlqkSnOhj7n8LAU/UDhulWHR/ymlJ3iOke3Ab2KaWucZwz\nxL0zhveSVslw6pQ+l853qW8co2meblvVsFQ5i1I6UWdupjbnGscO932qzJXSMhhq3ulnCnPmnGJo\nOZdJqd9pbFJKD1/vE6UUrTRHOuMwVe3htSx9F48N9xH7n6qG8TyTvpftTnWwubYhHYz3PiktIoXz\nd1L4OQ7TuZ/L8N4npSGyzfQmtnm/ynuxVDUsXdO5npQKyvMMr0tsp3TR9X3CbUgpG0yz4edXXHHF\n1ObxS8eY+5H7gudH3h+k+3Je+1ihj+kw3Bc8H/FaT+wPjwG3/d577924TvY5VUFOFZEOEp4/0r0A\nj0OqeMbjfPfdd0/tO+64Y2qn+xTuJ45/fi//TuQ+5rzj32Fc5pu/+Zun9kc/+tGpzfvJ9Pcc+5PS\nHHkfyr9zO3+HMg2rquq1r33t1P7ABz4wtdN9P+cRt4fHkvOXfz/ecMMNUzvtRx5vzgXOO+I5i8cs\nnQdTJWvuX6bIdiq9cSymVNC5183tuhuWJEmSJEnSSfEhkCRJkiRJ0gKc1nSw9Pb1Tth+SkXphPmn\nUFhiGBvD0FKIO8PQGHbKEDiGjHF5pjukVLW0T1LoN/vcrX6T0gwYosYQQH53quCSKh2kt8+ndDC+\nST+F0Kc3+Kd9lCocMSx7t/S5OVLY8EGV9ivHUkrBSeH/qaIHMZw5VQOcWw0jpcyk6ir04IMPTm2m\nU6Q5xfNDquDBEFSONfbn4x//+HHrTeG8Kdyf8zFVWGGoedqPDH0/cuTI1OY5gf1hmCv7w/Qh9ofr\nvP/++zf2LaXApDE6t9JQJ43wIOmkgKXP0xzppOsxBYHHkOtM6WApJSSl83KdKWUmpU3wXMvt4nWW\n+5DXYvYthVqvpwmk6iypClqqFMZ+p/mY0oQ4B7kNKY3lgQcemNo85/KYpWs6+8/tTRXg5l4351aj\nPEhSCklaJo0FStdfjv9O1TDqVLxNKWy8d2MqA8dgGstpnewzr5Ucg7wGcv27pSmn1GaOW87NVNUr\npep10v8oVZZK965pXzBVhMukipzputn5+4H97FZlO4hSKnFKZUpVELmej33sY1P7rrvu2rg89xnH\nWvr7hmlDKd2Wx5/r5N+brN6V7g04FjqVDTn2U5o2x0Lat1VVN91009Tmvr7zzjunNvcj7y2Zrsbv\n4BzhPmVVs1RVlPc373vf+zZ+nu6TeO7jd/E8k9LBuN/TNS5VB9tLFbDESCBJkiRJkqQF8CGQJEmS\nJEnSApyx6mAp5J86qWEplJLhY0xloPRWcq4/hTkzHIzrZ9hXSt1IqUspTCxVGmIofgpHTKHlVceH\n+fIYXHPNNRu/jyHlxPWm6jwplJTVChjOx9BJ7jtucwo7TiGvlEIhUzhjCt1OVWdSat82SCGjnfD8\ntAz3DXF8zQ2PT8uk80wK32abaSYMR++kOKR+pjBzjuXbb7/9uHUxRDZVAkpVZ1L1lFShjf1mZZB7\n7rln43o4Zw8fPjy1uX+ZVsfzBiuisQpDqj6VpBTRlOq1zelgaeylcxWPVUpB4PUrpSrz+PCYdFIB\nuDy/N6VisZ/p2spzBdMyKFWtYQg9r9FcP+8ZOA/W93NK9WRqM+cI7RYufwznNVNaeL1OKaLcvwxr\n5/zitqWUcO5HrjOldbPPtG2V+OaaW802pVTwPiKNES7DlD7eH/KawvWkMcJ7YB7/lBqYUiZTula6\nf0jXH37OinaUqrmuf1/nPJ9SotJ1M70qIm0n9xHPranqcCetMt278rs6+6FT5ZBSmthBlfqYUmq4\nPI8D9ys/598orCzHcydfCcKKVZ17bC6T0hY531M6W5L+Jk3XBI4Ljt+0zHqqLK8dX/M1XzO1X/3q\nV09tbmdK4T569OjU5rWM9xOdVDfOR6bb8e/QVB3tla985dRmSl7nOtj52zDNtU46mNXBJEmSJEmS\n9Cw+BJIkSZIkSVqA05oOlqTQ0BTulFI52GY4NsNlGVbG72VYHdspNJ3r59vBU6heSstKoaPcFoa5\nMWSd+4dha3xLPEPUuUzV8aHm6c3nDGe8/vrrpzZDzbk8w/BS5RiG1XHfpQpEHBOpaktK/Uih1ali\nTXqTfAr1TpVpuPw2hM52QrhTdT+am17TqfLUWSf7nPY9j09KOUohuB0prJ0h9wxf5VhbH18phSz1\nKYXCco4wxD2FOKeQ6JQqkMYN0+oY1s8290WqXMZ2ClPfy/zqpHCcaSlsmzrh3OmaksYOr5s8Vjw3\np0peKfUsVQfjfOEyvFYwBYz9SaHp3K507Wab35XWWZUrJzE1kte1lM6dUjl4XuB6WIWU9wFsp2pl\nnLMppJz7i8cs3QOlCmh7qVqyDddKStvaSeXhuEhpFFw/76eYEsFUBrZT6hmPVaqOx/s1Lp8qCXau\n0enVDUzL4LWyk/K5fk3gmEyvO0j3B2l7UgW9dJ1lO20P0/m4/UkaN1wn+5kqyaVqr510uc516CBJ\nr8hI57/0txs//7qv+7qpffPNN09tnne5z1K6VkqX5nelVO70WoNUybpzPk7pY/xdrj+dB7hd66ma\nqQJmqmqXKiPyOzjfuZ5OZXFeH9/61rduXA+vdyl1ML32JM2v9LoKLsO+pdSwucc4MRJIkiRJkiRp\nAXwIJEmSJEmStACnNR0shTgxpCuFbjEUjVI4K8MkmRLF0Gl+znAzthl6yreAp7SRFObK7UppTAwd\nZHjh61//+qnNyl0M4bv66qunNsPNGJK3nkrCfZrSPfj7N91008blGS6eKlVwm5mGxnA7bnOqdEBc\nnmH26VhyW/g2/+uuu25qMz0ghUhynKV2Sk86qNI+TmGGe6mqNPvt9Y3qYHv53rlVFdI6OyGZKaR6\nPW0xhTKnkFzOWZ53UjpYSuPiGGYoe6daIdfD8y/Xw3Y6P7DN7U2pcJ0UqRQSvw1h7Sl9IR1Dpv2m\nKhm8rvH8ze9idTAeh5TSxWsxr+np+psq2nEMpqpeaQym+4FUBYwpVintmOusOj5FiyknvD/gd6T+\npfQA9rtTyYzbwOVZ3TClnacxkdIFeTzY7pw3554rt2FucrvnXps4p9L5L6WDscpiOgen81+qNNSp\nYJOuX+nzlDbBbWFqVErzTK9TWE85SevidnbmHZdPxylVUOO2sQ8pHSy9siD9/ZP2HY8r7wHSuOxU\n4TwV938HSUqdTulEHCPpVQbp/iJVEk7nkJR+1Kmmnc61+3X/3Jnj6+M6nc/Ta1DS8ul1Kp1XybBP\nvK5deeWVG9eZ7hs7Ukpw5++QTuWvvaSAkZFAkiRJkiRJC+BDIEmSJEmSpAU4cNXBUjpYWj61U5Un\nhvOlcPRUqYNh1+xbCrlP20WdMGCGnKdqHocOHdrYnxQGW3V8SH0K52aqAJdJVUi4DQxbZRpI+t30\n1vv0lvxU8SJV9WIIPVPS2E4VmzrtbUsz6eiEudLcEMX9CmmkznjZr7DKzno61Q/WQ0dTBYEkpU+m\nkO9OhQKeH/g5vytVIWEof2p35jvPFUkKm+7st1Mx/vZbCk/uVO5L1wue83hd47FlqgHP36nSFq8V\nTAFjO12XeW3hMhdffPHGbeF+YNoT+8bUKJ77Uwpy2p/r10aulz9jmlhKcU9VL1OVSX5XSiXjOnk8\neE/AVJSUPsTv4n7nOrnv1lNxjknn3E51SdqGudnZDkrXzVSBimOBx5DjhefglB7Bdab+8Fjx/MDv\nSumD6fydzl28N2Ta6WOPPTa1mUqVtmt9bqZKhKlya5p3vC/lfGG/mXLF45eqGPJ8ymU6x4xV37jO\n9OoHjgn+bqcqVdpXc6vBbZt0fUlpXOn1D1wPj0magymNK+1vHtt0Tp1brW9uKlJaf0p/261/6Rya\nKptyjqf0s7Rf0t/qnfuqlIbXqTaX9t1eqiN3XoPQYSSQJEmSJEnSAvgQSJIkSZIkaQEORDpYCsVK\n4WMprSOFTaXQvk7oFvvAMDzi93YquTBEMFVn4OcpZYzLMNSUUiWX9b6y3QljSxVGKFUsSmG3KSyS\nfUvpLQzNZNg80wC4Ti7DPvDYcJ1J2vZtqAhGnfSuFEZNKc2oE6LYSeOizjKdUM3O+ufqVI7YTSel\nK4W5JunYcF+klK7Un5SKkOZ+qqiS+pNSdveSbtkZxwdJOv+ndNiUapCqS6X0PqY+sM3fZUUshmkz\nNYqfpzmeKnm9/OUvn9opHYrbzr4xpYn94frTNTB9V9Xx1wimSrHNdBoep5Q6nVK4maKVUlTYP24n\n18n7A7bZN+47Vs+8/PLLN/YnpWCnc1y6nqblt+Eams7znUo6PD5pDqYqXZyP6RUEKcUnnct5fuU5\nJKXOp2pa3JZ0jWKK0tGjR6f2kSNHpjbTxDqVgquOnyOpcut6tb9NfeV3MBWW/U5pzlyG+5Gfs925\nf+byTH/lPuV5NvWf44bSKxTS3wXbpjMf0z0F51Sq+pvSj9LfMZ3qWgnnV6qaN9deXmWR0rDWx3Xn\nXJnmQvo7pHOPnvZvqqTYSfuidF1Lxzil282t4tZ5HUCHkUCSJEmSJEkL4EMgSZIkSZKkBTit8X2p\nakmqtJV0wof5Bn1K1QBSClRKFaL0xvH0vVy+k67CPqT0LlYj6VRwqDo+jI3fzf6l0NuU3pa2jd+V\nwk25nSlEkO20Tob7M10hhc6m6jVsp7fQE/dhJ5XsIEmVmmi3ijmbzE3pSmGlp6KS19x+Jp3QyxSC\nuptOiOzcPnXSVlPKJ1O60pzlsUnVFjvVHDrS8inEl7atcl9n/HRCyjlnmRLBawrHDlN/mB7Eaw1T\niHi+TFWwOmmnvGaxb7wWsaIZz7XsG9Oz2E8uw+9KqTTradTcHlbu5HpTxS6ul9vDFCCuP1UWJO53\n9oGpYdwGzlluc6qsxhSglEbdmVNzKzVuWzpYumale6K5If+dFPxUeaaTds/fZXoTxybTm7jONK7T\nMeR6Hn744anNVCemMaVr1/rcTOOTy3E8U9ovKdUr7SOmXKVKYfxd9pNSNTVWUOO+5no45rgf2U5j\nKL0OYy/p8Wdaqn6Vxn+61+28moI66WZpeepUmt6vim2d+6aTubdK93up33PvCVOaWOf+o3PPnI7B\n3L9V5qYjnmrbO6slSZIkSZLU5kMgSZIkSZKkBThjr3tnCHdK90lVw1LIawqXTVVIUmoRQ9AZCphC\n0ztvOufynTfMpxDRVGmEbX5vCgPu6oROpuprqeLA3O/lsUmh1dxOhvsyHSylyXF59pNh9p3wxb1U\nxjrT0rhN1evSHEzhkyn9iFJqWCdEdm64bCd0dr/Ca5Pd5iPPF51KEnMrhXVSWFOqA9spRZb9n5sO\nlyoocSx2QoU71SK3oTpYqvyQUvdSZasUIp2uiUwtYoUorp/HP53/OqmmneOQzvE8lzMNi6laKY2a\n60zVP9fTRzgHuY9S2gDTrLjfU7W7lEqUqpcRtz/NtVQNkBFxNOsAACAASURBVNvJ7UppP2k96d6r\nk0bd+fwg6VR07dzTJuk+gmOnc2+V7td4nubnHLNMXepUdqWUHsF1Pvjgg1M7vcYhjaP1alf8/XRt\n4hzp3MvxWKYUMLaZMsb+8XPu91RljdvJtDKmg/HzdB5PVR5TmnY6lp00v23TubfqVJHu3H92pGs0\n53tKYd5LullnmbS93SpYqQLX3OtC6l86z6YU45T2uJfXCHRSw/byeou5Fcc6jASSJEmSJElaAB8C\nSZIkSZIkLcBpTQfrpISkZVKKUwp5TmHqDJnshMd30rVSBS1K6VrEUEOG23FbUihgCs3dLY0phcCl\nqh9p37FPXIa/m6qfpDC2lN6S3gBPrGySxhD3KVMFuP4UQt2paNUJLzxIOqkDlPYHpYpzSUrXSmll\nc9O70u+mVCfayzHsVIrbLTUsbVsKDe2cjzrn4k5IbeeYdaohdsJZ51awSyHH25ACRp0x2dk3KeQ/\nXStTalgaz52qcZ0KIenaksKf03WZaR+pP5TOM+vpYOwf02Y6lbx4Pk3nRH7Oqny0XhXpmE4lUe4X\n4vJsc7vSPUBKD+ikSaTjeqrTcU+lTiUvSmOB902d82u6XjMNKKUNdSouUkq3SmMhpVgxhYvjnfMu\njYX1z/n7TJtiGhe3P6U4cfyzr6miH/cR93VKH0vjI6WN8Hcff/zxqZ1eucB2qu6WzrkprZW2IVUz\nnWvTOSadq1KFvk46TtrHcytHpfu1dL1O29hJLe/0OZ3Tdruv7KQyddppezopip3UY7ZTyiTxmpuO\nR0rnS6/e6NivymJGAkmSJEmSJC2AD4EkSZIkSZIW4IxVB6O56RKdilUpNDu9iT+1iWGV6S3xDDtk\nnxmCzZDMFKaYwtdTGB1Dc7uVglJaE/dRCgFMIfvcL52KaNRJMUr4vQwjTseGOFa4DEMB03GitN+2\nAcOoU9hjqlyRdCoApHTLdPzTejqpMZ3Q95Qims4z6TzQSWvopnClbUvhvCm9J43hFEqaqmKkZWhu\nNbjU7owPhtp20qI6KTlni5Q2ktJ8uY95XkzXx1TlJl1neU5N/ST+bhrXbHP5lNbdqdqZ0uXW15v2\nUarc1qmwws85ttMcSeejlK6T0va4PNud1L79qo5D25BGnc4lc/dNOv5pzqbzXErr4BjkmEopkBxf\nKY0pjS+uP50T+L0pZeqiiy6a2ikNa/1+IKXPMUWL+5Hfze1hZUF+d0oPScuk5dPnKfWI+/TJJ5/c\n+LvpnJXSwVLl11SZl7ahOtjcak6U7j95TNJ86dxbpfujTjpYmrPJ3OWTueef/dSp4pfuxVM73d93\nqhRzHKRXsaTreLpPSP3v2Mv110ggSZIkSZKkBfAhkCRJkiRJ0gIciHSw9Gb6FK7F0CqGYlEnlI5h\nkqx+0nnrdlp/wn4yBDWFoHfSUrivUvWxtJ71/+6EvaWULoaMdqpidKoRpSoklEJh2eZxTWHNKYSP\n/UlpDJ10lW2oRpTSwbh9qTJdkkLZU9WwlNJIaV+mKgGdcN9O1YOkk06UUle639Wp6NCpzJTCedP5\nIp3X0jbMrdaWtitdA+ZWmKP9qqRwJnRCldOxSufyDh6HVAGSfUgp2CllJo2XdK7oVO1M4yiN/ZTG\nsVsqZAr/nju/Uvo3darUENffqRiajlmqeJLmV7o36FQKS9s4NyX8TJhbPYnjbS/VmTopHik9LV13\neMw535lKle4HKVXFSeljvPdIVWQppVms475OFW+5banaV7onTPuXn6f1pPvPVNUrpTyn+3Yuz/3A\n7b3gggs2/m66D9+2NOq9VCZM6+noVJrq3KPul7mVyNLvpjHbvZ/q7Md0f7sXnerFnddVpNcppNdM\nsJ2qeaZrX2efpmvr3JS/g3+VlSRJkiRJ0p75EEiSJEmSJGkBTms6WArtTxUtOuHiKYy2U+2IOlU4\nUvh9ajMENaU3pVD2TqWVFAKWwrd3Sz9JIf4pXJ7HKYU4d9IVOA7OO++8qT33zf5pPzIMb24qAsdQ\nCoVMKQfbUD2BGHrM7Uhh1HsJ1UzhmfyudKySueGTHLOdENRUHatTEYy/m8ZsWv9u30edkOK5aW9z\n0zdSaHqaa50UpnQ+SX1L58cUrr9tUtpjqryTjvPccO50jqSUxtJJHaZOxblOqmK63+ik6u5WCTGd\nL1IlyrRMp/JZOqeke6C0j+amXnZScU5VJZhtkqo8pfuCTpWbzrm8M8c785rXwfSagk5qVEcaOymV\nMPUnXXOqchWeTsUmfgfv/TrHL83NNB/T/Erbye1KaWjp/JNea5BS+zrVE7dh7nfSwebeN1HnXozH\nPFWJTPdWne+idF3qVJeee1/ZqdC9Wzrv3NSwzj5K/ZubbpeurZ3qz2k/plTYzutmOp/PXYaMBJIk\nSZIkSVoAHwJJkiRJkiQtwGlNB+uEKaWQqxTGmELvWPWiU12rU4GFIaIpzC+lffHzuWk1aT17rWTE\n9XIbXvKSl2zsK1OG2A+mXLFPKaQ+VUvZyxvRKYXgMqSWxzKFPqew2LMxVD6FIT/11FNTu5MSQqlC\nUOdt/XNDWDsVtCiFy6YKRFw+naPSNu4lhH5dOt9RmoMpXadTkbETXpsqKaT0q93C+jd9VwqnTyHI\nnbD2bUgNm5selY5bp0pVmkedY5X63KnUSZ0x2Ll2U2eMp9TX9bS19PudtK807yiN+ZR2nVIg031D\nJy0hSeMmVTbZSyrvNlTxY8pSwv2U0gKok66UUoVSWiilc2e61qcKchyPSUqvT7h/WMmq8+qDql5q\nXCctq3OO66S8dqqWpoq66f68U+UxXdf4vdy/6fUcPMbbMB+pU20pzYW55610j5fmfjqG6bpG6bv4\nu6kSWfrddB1M4zGlV9N6/+emuc5N4evcr6b1p/vGzqsPaG4F5U4FzLSe/aoqZySQJEmSJEnSAvgQ\nSJIkSZIkaQFOazpYCpNkCBWlt9EzXC2FTHYqY3Qq9aQQXOpU5krh2ymMNFUZS5VAOulm66Fn/G9+\nR+f70n5kP1784hdP7fPPP39q83iznUIS56YJUQrL5/d2Qm07aSnbLFXHS9VPOiHJKTUypSilMExK\nx7+TppDWmdImUj87FcRSalQ3/aITRp5CvjvHqVPJbG7FiE6obTonpmU6aYSdlNI0PrbB3LRXSnM5\nndcZ/p/GQkqtmFsRrnM8Uzh6Ck1P/eycT1I62LrOd3TuMzrptZ3qZWm/dKqQprHVObem+d6Z150+\nbLNOZcV0jaD0u+l4UifN7lTfv8z9rpRy0a32k85He9GteHRMJ2W7s/zc+9uOlILarU66TTinOqnE\n+3VfkNKsOtWlOjopjGnMpvnFNtM/56bO73afvF/7upPmmr63k2LX+d1OOz1H2IuUttm5b0/Ojr9g\nJUmSJEmStCsfAkmSJEmSJC3AaY0BTCk+KRSR1QRS2HZ6I/rcEOMUgs7wzAsuuGBq8836nfC89Bb3\nTih3aqf0KW47w8fWQ1NTP1glohP6zxSClKJw0UUXbfzdlJaQUmtSf9K+Jo4hhjyywkcn1Se1O1XD\nDqqUvjE3LYtSWGwK7e6Ef+8lxDudQ6jT51QVpRPy2UkfWf/vTmpBCvPtpHSludZJ8UhhqHOroKXz\nwFwpNDedQ7dZJ70xLZ+OIcf23GpR1EkHS+vppF6msZl0tiWlcq/fn3RSLKkTpp72V5pHe5kje6nY\nNTet7GxP++qcpzvV+jqV+6hTxa+jk3a8lxSruef1znzfLTVsbjrVqdC5Dqb9kq7daUx0rrOdOZj6\nsM3XynQ/yfN5J7117r1oeo0JX0HRmdede+O5lRjTdSbdu3ZSsLvXhE6K2tyU0fR5p3Ll3PWn+5JU\nqZHV4ParclknNXVu6tn2znBJkiRJkiS1+RBIkiRJkiRpAYb9eoO+JEmSJEmSDi4jgSRJkiRJkhbA\nh0CSJEmSJEkL4EMgSZIkSZKkBfAhkCRJkiRJ0gL4EEiSJEmSJGkBfAgkSZIkSZK0AD4EkiRJkiRJ\nWgAfAkmSJEmSJC2AD4EkSZIkSZIWwIdAkiRJkiRJC+BDIEmSJEmSpAXwIZAkSZIkSdIC+BBIkiRJ\nkiRpAXwIJEmSJEmStAA+BJIkSZIkSVoAHwJJkiRJkiQtgA+BJEmSJEmSFsCHQJIkSZIkSQvgQyBJ\nkiRJkqQF8CGQJEmSJEnSAvgQSJIkSZIkaQF8CCRJkiRJkrQAPgSSJEmSJElaAB8CSZIkSZIkLYAP\ngSRJkiRJkhbAh0CSJEmSJEkL4EMgSZIkSZKkBfAhkCRJkiRJ0gL4EEiSJEmSJGkBfAgkSZIkSZK0\nAD4EkiRJkiRJWgAfAkmSJEmSJC2AD4EkSZIkSZIWwIdAkiRJkiRJC+BDIEmSJEmSpAXwIZAkSZIk\nSdIC+BBIkiRJkiRpAXwIJEmSJEmStAA+BJIkSZIkSVoAHwJJkiRJkiQtgA+BJEmSJEmSFsCHQJIk\nSZIkSQvgQyBJkiRJkqQF8CGQJEmSJEnSAvgQSJIkSZIkaQF8CCRJkiRJkrQAPgSSJEmSJElaAB8C\nSZIkSZIkLYAPgSRJkiRJkhbAh0CSJEmSJEkL4EMgSZIkSZKkBfAhkCRJkiRJ0gL4EEiSJEmSJGkB\nfAgkSZIkSZK0AD4EkiRJkiRJWgAfAkmSJEmSJC2AD4EkSZIkSZIWwIdAkiRJkiRJC+BDIEmSJEmS\npAXwIZAkSZIkSdIC+BBIkiRJkiRpAXwIJEmSJEmStAA+BJIkSZIkSVoAHwJJkiRJkiQtgA+BJEmS\nJEmSFsCHQJIkSZIkSQvgQyBJkiRJkqQF8CGQJEmSJEnSAvgQSJIkSZIkaQF8CCRJkiRJkrQAPgSS\nJEmSJElaAB8CSZIkSZIkLYAPgSRJkiRJkhbAh0CSJEmSJEkL4EMgSZIkSZKkBfAhkCRJkiRJ0gL4\nEEiSJEmSJGkBfAgkSZIkSZK0AD4EkiRJkiRJWgAfAkmSJEmSJC2AD4EkSZIkSZIWwIdAkiRJk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5J1yG38t+8ne5PPHzP/qjP9r4XakP6YXdXJ7ttB/Y5vLr//385z9/anM7uQ1PPvnk1OZ++Yqv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\n9OcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4485a5a978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = (20.0, 20.0)\n",
    "f, ax = plt.subplots(nrows=3, ncols=5)\n",
    "\n",
    "im_samples = []\n",
    "\n",
    "for row in range(3):\n",
    "    for i, j in enumerate(np.sort(np.random.randint(0, c4_target_val.shape[0], size=5))):\n",
    "        im = c4_data_val[j].reshape((64, 64, 1))\n",
    "        house_num = ''\n",
    "        for k in np.arange(c4_target_val[j,0]):\n",
    "            house_num += str(c4_target_val[j,k+1])\n",
    "        house_num += \"--\" + str(j)\n",
    "        im_samples.extend([j])\n",
    "        ax[row, i].axis('off')\n",
    "        ax[row, i].set_title(house_num, loc='center')\n",
    "        ax[row, i].imshow(im[:,:,0], cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(1, 20, 3, padding=(1, 1))\n",
    "        self.conv2 = nn.Conv2d(20, 40, 3, padding=(1, 1))\n",
    "        self.conv3 = nn.Conv2d(40, 80, 3, padding=(1, 1))\n",
    "        self.conv4 = nn.Conv2d(80, 120, 3, padding=(1, 1))\n",
    "        self.conv5 = nn.Conv2d(120, 160, 3, padding=(1, 1))\n",
    "        self.conv6 = nn.Conv2d(160, 200, 3, padding=(1, 1))\n",
    "        self.conv7 = nn.Conv2d(200, 240, 3, padding=(1, 1))\n",
    "        self.pool = nn.MaxPool2d(2, 2)\n",
    "        self.drop2d = nn.Dropout2d(0.25)\n",
    "        self.drop1 = nn.Dropout(0.35)\n",
    "        self.drop2 = nn.Dropout(0.5)\n",
    "        self.bnorm1 = nn.BatchNorm2d(80)\n",
    "        self.bnorm2 = nn.BatchNorm2d(120)\n",
    "        self.bnorm3 = nn.BatchNorm2d(160)\n",
    "        self.bnorm4 = nn.BatchNorm2d(200)\n",
    "        self.bnorm5 = nn.BatchNorm2d(240)\n",
    "        self.FC = nn.Linear(960, 1080)\n",
    "        self.digitlength = nn.Linear(1080, 7)\n",
    "        self.digit1 = nn.Linear(1080, 10)\n",
    "        self.digit2 = nn.Linear(1080, 10)\n",
    "        self.digit3 = nn.Linear(1080, 10)\n",
    "        self.digit4 = nn.Linear(1080, 10)\n",
    "        self.digit5 = nn.Linear(1080, 10)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        x = F.relu(self.conv1(x))\n",
    "        x = self.drop2d(x)\n",
    "        x = self.pool(F.relu(self.conv2(x)))\n",
    "        x = self.drop2d(x)\n",
    "        x = self.bnorm1(F.relu(self.conv3(x)))\n",
    "        x = self.pool(self.bnorm2(F.relu(self.conv4(x))))\n",
    "        x = self.drop1(x)\n",
    "        x = self.pool(self.bnorm3(F.relu(self.conv5(x))))\n",
    "        x = self.drop1(x)\n",
    "        x = self.pool(self.bnorm4(F.relu(self.conv6(x))))\n",
    "        x = self.drop1(x)\n",
    "        x = self.pool(self.bnorm5(F.relu(self.conv7(x))))\n",
    "        x = x.view(-1, 960)\n",
    "        x = self.drop1(x)\n",
    "        x = F.relu(self.FC(x))\n",
    "        x = self.drop2(x)\n",
    "        yl = self.digitlength(x)\n",
    "        y1 = self.digit1(x)\n",
    "        y2 = self.digit2(x)\n",
    "        y3 = self.digit3(x)\n",
    "        y4 = self.digit4(x)\n",
    "        y5 = self.digit5(x)\n",
    "        return [yl, y1, y2, y3, y4, y5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Net (\n",
       "  (conv1): Conv2d(1, 20, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv2): Conv2d(20, 40, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv3): Conv2d(40, 80, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv4): Conv2d(80, 120, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv5): Conv2d(120, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv6): Conv2d(160, 200, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (conv7): Conv2d(200, 240, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "  (pool): MaxPool2d (size=(2, 2), stride=(2, 2), dilation=(1, 1))\n",
       "  (drop2d): Dropout2d (p=0.25)\n",
       "  (drop1): Dropout (p = 0.35)\n",
       "  (drop2): Dropout (p = 0.5)\n",
       "  (bnorm1): BatchNorm2d(80, eps=1e-05, momentum=0.1, affine=True)\n",
       "  (bnorm2): BatchNorm2d(120, eps=1e-05, momentum=0.1, affine=True)\n",
       "  (bnorm3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True)\n",
       "  (bnorm4): BatchNorm2d(200, eps=1e-05, momentum=0.1, affine=True)\n",
       "  (bnorm5): BatchNorm2d(240, eps=1e-05, momentum=0.1, affine=True)\n",
       "  (FC): Linear (960 -> 1080)\n",
       "  (digitlength): Linear (1080 -> 7)\n",
       "  (digit1): Linear (1080 -> 10)\n",
       "  (digit2): Linear (1080 -> 10)\n",
       "  (digit3): Linear (1080 -> 10)\n",
       "  (digit4): Linear (1080 -> 10)\n",
       "  (digit5): Linear (1080 -> 10)\n",
       ")"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net = Net()\n",
    "f = open('c5.pkl', 'rb')\n",
    "net.load_state_dict(torch.load(f))\n",
    "f.close()\n",
    "net.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.FloatTensor torch.Size([6227, 1, 64, 64])\n",
      "torch.LongTensor torch.Size([6227, 6])\n"
     ]
    }
   ],
   "source": [
    "c4_data_tensor = torch.from_numpy(c4_data)\n",
    "c4_target_tensor = torch.from_numpy(c4_target).type(torch.LongTensor)\n",
    "print(c4_data_tensor.type(), c4_data_tensor.size())\n",
    "print(c4_target_tensor.type(), c4_target_tensor.size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.FloatTensor torch.Size([159, 1, 64, 64])\n",
      "torch.LongTensor torch.Size([159, 6])\n"
     ]
    }
   ],
   "source": [
    "c4_data_tensor_val = torch.from_numpy(c4_data_val)\n",
    "c4_target_tensor_val = torch.from_numpy(c4_target_val).type(torch.LongTensor)\n",
    "print(c4_data_tensor_val.type(), c4_data_tensor_val.size())\n",
    "print(c4_target_tensor_val.type(), c4_target_tensor_val.size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "objective = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(net.parameters())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "97\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "num_epochs = 20\n",
    "batch_size = 64\n",
    "num_train =  c4_data.shape[0]\n",
    "num_valid = c4_data_val.shape[0]\n",
    "iter_per_epoch_tr = num_train // batch_size\n",
    "iter_per_epoch_val = num_valid // batch_size\n",
    "print_every_tr = 60\n",
    "print_every_val = 2\n",
    "print(iter_per_epoch_tr)\n",
    "print(iter_per_epoch_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tr_epoch_losses = {i:[] for i in range(num_epochs)}\n",
    "tr_loss_history = []\n",
    "val_epoch_losses = {i:[] for i in range(num_epochs)}\n",
    "val_loss_history = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0/19\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  97\n",
      "loss :  55.43372344970703\n",
      "lossl :  24.333772659301758 loss1 :  5.462127685546875 loss2 :  7.622984409332275 loss3 :  9.03563117980957\n",
      "loss4 :  8.979208946228027\n",
      "Iteration :  61  /  97\n",
      "loss :  8.240893363952637\n",
      "lossl :  0.07589834928512573 loss1 :  1.1425186395645142 loss2 :  2.3618083000183105 loss3 :  2.3699698448181152\n",
      "loss4 :  2.290698289871216\n",
      "time taken :  151.54585099220276\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  2\n",
      "loss :  8.369678497314453\n",
      "lossl :  0.027089890092611313 loss1 :  1.7536942958831787 loss2 :  2.197176218032837 loss3 :  2.168215751647949\n",
      "loss4 :  2.2235021591186523\n",
      "time taken :  1.1990385055541992\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 1/19\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  97\n",
      "loss :  8.345032691955566\n",
      "lossl :  0.03583906218409538 loss1 :  1.2855385541915894 loss2 :  2.2493667602539062 loss3 :  2.437037229537964\n",
      "loss4 :  2.3372507095336914\n",
      "Iteration :  61  /  97\n",
      "loss :  7.91580057144165\n",
      "lossl :  0.024120867252349854 loss1 :  1.0396851301193237 loss2 :  2.2954564094543457 loss3 :  2.304487705230713\n",
      "loss4 :  2.2520503997802734\n",
      "time taken :  118.81673169136047\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  2\n",
      "loss :  8.142087936401367\n",
      "lossl :  0.01068069040775299 loss1 :  1.6154155731201172 loss2 :  2.186333656311035 loss3 :  2.138477087020874\n",
      "loss4 :  2.1911814212799072\n",
      "time taken :  0.5234143733978271\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 2/19\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  97\n",
      "loss :  7.727816581726074\n",
      "lossl :  0.015116019174456596 loss1 :  0.9146196246147156 loss2 :  2.2875382900238037 loss3 :  2.2495133876800537\n",
      "loss4 :  2.2610294818878174\n",
      "Iteration :  61  /  97\n",
      "loss :  7.616324424743652\n",
      "lossl :  0.012179231271147728 loss1 :  1.2017792463302612 loss2 :  1.9803446531295776 loss3 :  2.235563278198242\n",
      "loss4 :  2.186457872390747\n",
      "time taken :  134.51576590538025\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  2\n",
      "loss :  7.683386325836182\n",
      "lossl :  0.004311034455895424 loss1 :  1.610594630241394 loss2 :  2.0601089000701904 loss3 :  2.028747320175171\n",
      "loss4 :  1.9796241521835327\n",
      "time taken :  0.6333363056182861\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 3/19\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  97\n",
      "loss :  7.113443374633789\n",
      "lossl :  0.002815255895256996 loss1 :  0.7610573172569275 loss2 :  2.0410358905792236 loss3 :  2.2205426692962646\n",
      "loss4 :  2.087991952896118\n",
      "Iteration :  61  /  97\n",
      "loss :  7.009838104248047\n",
      "lossl :  0.0055778734385967255 loss1 :  0.8978535532951355 loss2 :  2.040893316268921 loss3 :  2.086320161819458\n",
      "loss4 :  1.9791932106018066\n",
      "time taken :  136.75901770591736\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  2\n",
      "loss :  6.857181549072266\n",
      "lossl :  0.0019062235951423645 loss1 :  1.3098077774047852 loss2 :  1.8136909008026123 loss3 :  1.90603506565094\n",
      "loss4 :  1.8257418870925903\n",
      "time taken :  0.6182096004486084\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 4/19\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  97\n",
      "loss :  6.627264499664307\n",
      "lossl :  0.0022597070783376694 loss1 :  0.7709953784942627 loss2 :  1.9241031408309937 loss3 :  2.0743181705474854\n",
      "loss4 :  1.8555885553359985\n",
      "Iteration :  61  /  97\n",
      "loss :  6.361793041229248\n",
      "lossl :  0.0005066506564617157 loss1 :  0.8067440986633301 loss2 :  1.7644691467285156 loss3 :  1.912611722946167\n",
      "loss4 :  1.8774614334106445\n",
      "time taken :  139.54969453811646\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  2\n",
      "loss :  6.01300048828125\n",
      "lossl :  0.0002118535339832306 loss1 :  1.3503990173339844 loss2 :  1.5127027034759521 loss3 :  1.5901625156402588\n",
      "loss4 :  1.5595245361328125\n",
      "time taken :  1.2347347736358643\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 5/19\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  97\n",
      "loss :  6.551087856292725\n",
      "lossl :  0.0015625972300767899 loss1 :  1.00276780128479 loss2 :  1.6997950077056885 loss3 :  1.9779013395309448\n",
      "loss4 :  1.8690608739852905\n",
      "Iteration :  61  /  97\n",
      "loss :  6.049417495727539\n",
      "lossl :  0.00023580342531204224 loss1 :  0.678046703338623 loss2 :  1.6577298641204834 loss3 :  2.034141778945923\n",
      "loss4 :  1.6792631149291992\n",
      "time taken :  146.70156502723694\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  2\n",
      "loss :  5.245006084442139\n",
      "lossl :  0.00019188225269317627 loss1 :  1.0073524713516235 loss2 :  1.4381848573684692 loss3 :  1.3555635213851929\n",
      "loss4 :  1.4437133073806763\n",
      "time taken :  1.2485804557800293\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 6/19\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  97\n",
      "loss :  5.824563026428223\n",
      "lossl :  0.0030418112874031067 loss1 :  0.8151108622550964 loss2 :  1.6757742166519165 loss3 :  1.8146380186080933\n",
      "loss4 :  1.5159977674484253\n",
      "Iteration :  61  /  97\n",
      "loss :  4.932307243347168\n",
      "lossl :  0.00014587119221687317 loss1 :  0.5723645091056824 loss2 :  1.52305269241333 loss3 :  1.4733867645263672\n",
      "loss4 :  1.3633573055267334\n",
      "time taken :  163.35150837898254\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  2\n",
      "loss :  5.735538482666016\n",
      "lossl :  0.00016507133841514587 loss1 :  1.6689714193344116 loss2 :  1.377776026725769 loss3 :  1.3496798276901245\n",
      "loss4 :  1.338945984840393\n",
      "time taken :  1.0154097080230713\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 7/19\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  97\n",
      "loss :  5.327887535095215\n",
      "lossl :  0.002085841726511717 loss1 :  0.6706571578979492 loss2 :  1.5165718793869019 loss3 :  1.7744460105895996\n",
      "loss4 :  1.3641266822814941\n",
      "Iteration :  61  /  97\n",
      "loss :  4.912391185760498\n",
      "lossl :  0.0002382434904575348 loss1 :  0.5137755274772644 loss2 :  1.1925307512283325 loss3 :  1.6051746606826782\n",
      "loss4 :  1.6006718873977661\n",
      "time taken :  154.70642137527466\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  2\n",
      "loss :  3.689659357070923\n",
      "lossl :  8.143484592437744e-06 loss1 :  0.6377958655357361 loss2 :  1.104361891746521 loss3 :  0.952984094619751\n",
      "loss4 :  0.9945094585418701\n",
      "time taken :  1.1423218250274658\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 8/19\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  97\n",
      "loss :  4.610106945037842\n",
      "lossl :  0.0004956163465976715 loss1 :  0.5686237215995789 loss2 :  1.2709062099456787 loss3 :  1.6488351821899414\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss4 :  1.1212462186813354\n",
      "Iteration :  61  /  97\n",
      "loss :  4.293152809143066\n",
      "lossl :  0.00038358569145202637 loss1 :  0.5934885144233704 loss2 :  1.194040060043335 loss3 :  1.1778841018676758\n",
      "loss4 :  1.3273563385009766\n",
      "time taken :  165.50367999076843\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  2\n",
      "loss :  3.935894250869751\n",
      "lossl :  2.4508684873580933e-05 loss1 :  1.3029234409332275 loss2 :  1.0243849754333496 loss3 :  0.8253818154335022\n",
      "loss4 :  0.7831794619560242\n",
      "time taken :  1.1569530963897705\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 9/19\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  97\n",
      "loss :  4.121510028839111\n",
      "lossl :  5.706772208213806e-05 loss1 :  0.30378228425979614 loss2 :  1.2111378908157349 loss3 :  1.2136014699935913\n",
      "loss4 :  1.3929314613342285\n",
      "Iteration :  61  /  97\n",
      "loss :  3.859370708465576\n",
      "lossl :  5.6918710470199585e-05 loss1 :  0.6834172010421753 loss2 :  1.0012606382369995 loss3 :  1.1808602809906006\n",
      "loss4 :  0.993775486946106\n",
      "time taken :  154.44419956207275\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  2\n",
      "loss :  3.3846161365509033\n",
      "lossl :  4.896894097328186e-05 loss1 :  0.892585277557373 loss2 :  0.8338439464569092 loss3 :  0.818076491355896\n",
      "loss4 :  0.840061366558075\n",
      "time taken :  1.2151672840118408\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 10/19\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  97\n",
      "loss :  3.4810097217559814\n",
      "lossl :  0.0006635561585426331 loss1 :  0.4100748896598816 loss2 :  0.8602355718612671 loss3 :  1.3151860237121582\n",
      "loss4 :  0.8948494791984558\n",
      "Iteration :  61  /  97\n",
      "loss :  3.486967086791992\n",
      "lossl :  0.0002949535846710205 loss1 :  0.46356502175331116 loss2 :  1.023413896560669 loss3 :  0.8840952515602112\n",
      "loss4 :  1.1155978441238403\n",
      "time taken :  142.5711748600006\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  2\n",
      "loss :  2.891064167022705\n",
      "lossl :  1.0482966899871826e-05 loss1 :  0.6492456197738647 loss2 :  0.5813660621643066 loss3 :  0.7802433371543884\n",
      "loss4 :  0.8801988363265991\n",
      "time taken :  0.5211324691772461\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 11/19\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  97\n",
      "loss :  3.1087875366210938\n",
      "lossl :  0.00010131672024726868 loss1 :  0.3593606650829315 loss2 :  0.8863365650177002 loss3 :  0.9406747221946716\n",
      "loss4 :  0.9223142266273499\n",
      "Iteration :  61  /  97\n",
      "loss :  2.662400484085083\n",
      "lossl :  1.448020339012146e-05 loss1 :  0.31195080280303955 loss2 :  0.7214779257774353 loss3 :  0.6840785145759583\n",
      "loss4 :  0.944878876209259\n",
      "time taken :  118.03396153450012\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  2\n",
      "loss :  2.6908061504364014\n",
      "lossl :  1.671537756919861e-05 loss1 :  0.7998747825622559 loss2 :  0.4939556419849396 loss3 :  0.7779064178466797\n",
      "loss4 :  0.6190527081489563\n",
      "time taken :  0.4960494041442871\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 12/19\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  97\n",
      "loss :  2.617784261703491\n",
      "lossl :  5.9157609939575195e-06 loss1 :  0.3213111460208893 loss2 :  0.6604998707771301 loss3 :  0.9389183521270752\n",
      "loss4 :  0.6970489025115967\n",
      "Iteration :  61  /  97\n",
      "loss :  3.1549415588378906\n",
      "lossl :  2.314150333404541e-05 loss1 :  0.2275654822587967 loss2 :  0.9651093482971191 loss3 :  0.874284029006958\n",
      "loss4 :  1.0879594087600708\n",
      "time taken :  117.93200635910034\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  2\n",
      "loss :  2.5507092475891113\n",
      "lossl :  0.00014580786228179932 loss1 :  0.6706263422966003 loss2 :  0.617202639579773 loss3 :  0.6188440918922424\n",
      "loss4 :  0.6438905000686646\n",
      "time taken :  0.49889588356018066\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 13/19\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  97\n",
      "loss :  3.1936893463134766\n",
      "lossl :  0.0005815364420413971 loss1 :  0.5577715635299683 loss2 :  0.7908919453620911 loss3 :  0.7186012864112854\n",
      "loss4 :  1.1258431673049927\n",
      "Iteration :  61  /  97\n",
      "loss :  2.770138740539551\n",
      "lossl :  0.00013813376426696777 loss1 :  0.4795672595500946 loss2 :  0.8861081600189209 loss3 :  0.6945767998695374\n",
      "loss4 :  0.7097484469413757\n",
      "time taken :  117.25979018211365\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  2\n",
      "loss :  2.4251019954681396\n",
      "lossl :  1.7881393432617188e-07 loss1 :  0.8306828737258911 loss2 :  0.6296924352645874 loss3 :  0.502872109413147\n",
      "loss4 :  0.46185430884361267\n",
      "time taken :  0.49608945846557617\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 14/19\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  97\n",
      "loss :  2.1122007369995117\n",
      "lossl :  2.391636371612549e-06 loss1 :  0.3380492925643921 loss2 :  0.5410863161087036 loss3 :  0.6483116149902344\n",
      "loss4 :  0.5847511291503906\n",
      "Iteration :  61  /  97\n",
      "loss :  2.2151341438293457\n",
      "lossl :  0.0002527162432670593 loss1 :  0.2802400588989258 loss2 :  0.47779226303100586 loss3 :  0.7111558318138123\n",
      "loss4 :  0.7456932067871094\n",
      "time taken :  117.27381491661072\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  2\n",
      "loss :  2.1443896293640137\n",
      "lossl :  7.666274905204773e-05 loss1 :  0.6226587295532227 loss2 :  0.5600907802581787 loss3 :  0.42854687571525574\n",
      "loss4 :  0.5330166220664978\n",
      "time taken :  0.49631547927856445\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 15/19\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  97\n",
      "loss :  1.890383005142212\n",
      "lossl :  0.0002833344042301178 loss1 :  0.2335541546344757 loss2 :  0.6142441630363464 loss3 :  0.5154803395271301\n",
      "loss4 :  0.526820957660675\n",
      "Iteration :  61  /  97\n",
      "loss :  1.8466235399246216\n",
      "lossl :  6.48200511932373e-07 loss1 :  0.2265690714120865 loss2 :  0.5223850607872009 loss3 :  0.4315939247608185\n",
      "loss4 :  0.6660748720169067\n",
      "time taken :  117.25413107872009\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  2\n",
      "loss :  2.7019262313842773\n",
      "lossl :  4.4852495193481445e-06 loss1 :  1.3239750862121582 loss2 :  0.39612746238708496 loss3 :  0.49025124311447144\n",
      "loss4 :  0.49156779050827026\n",
      "time taken :  0.49637937545776367\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 16/19\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  97\n",
      "loss :  1.6734997034072876\n",
      "lossl :  9.34302806854248e-06 loss1 :  0.22000378370285034 loss2 :  0.46988770365715027 loss3 :  0.5947712659835815\n",
      "loss4 :  0.38882771134376526\n",
      "Iteration :  61  /  97\n",
      "loss :  1.8545238971710205\n",
      "lossl :  2.522021532058716e-06 loss1 :  0.2828689217567444 loss2 :  0.5612845420837402 loss3 :  0.46215546131134033\n",
      "loss4 :  0.5482124090194702\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time taken :  117.30895066261292\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  2\n",
      "loss :  2.1221327781677246\n",
      "lossl :  0.0006055161356925964 loss1 :  1.083112120628357 loss2 :  0.2566189467906952 loss3 :  0.43506142497062683\n",
      "loss4 :  0.346734881401062\n",
      "time taken :  0.49924206733703613\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 17/19\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  97\n",
      "loss :  1.6897070407867432\n",
      "lossl :  0.000557679682970047 loss1 :  0.18942156434059143 loss2 :  0.33506086468696594 loss3 :  0.5586897730827332\n",
      "loss4 :  0.6059772372245789\n",
      "Iteration :  61  /  97\n",
      "loss :  1.5720717906951904\n",
      "lossl :  2.1532177925109863e-06 loss1 :  0.10716071724891663 loss2 :  0.4581289291381836 loss3 :  0.37766680121421814\n",
      "loss4 :  0.6291132569313049\n",
      "time taken :  117.31770038604736\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  2\n",
      "loss :  2.232828140258789\n",
      "lossl :  8.530914783477783e-06 loss1 :  0.7882530093193054 loss2 :  0.38090360164642334 loss3 :  0.6263383626937866\n",
      "loss4 :  0.437324583530426\n",
      "time taken :  0.5003798007965088\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 18/19\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  97\n",
      "loss :  1.9262685775756836\n",
      "lossl :  1.5623867511749268e-05 loss1 :  0.17121641337871552 loss2 :  0.5543897747993469 loss3 :  0.4725174307823181\n",
      "loss4 :  0.7281293272972107\n",
      "Iteration :  61  /  97\n",
      "loss :  2.1958401203155518\n",
      "lossl :  3.7711113691329956e-05 loss1 :  0.4161362648010254 loss2 :  0.6003668904304504 loss3 :  0.5932372808456421\n",
      "loss4 :  0.586061954498291\n",
      "time taken :  117.30226922035217\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  2\n",
      "loss :  1.8387413024902344\n",
      "lossl :  0.0008567739278078079 loss1 :  0.5644112825393677 loss2 :  0.4266436994075775 loss3 :  0.46165624260902405\n",
      "loss4 :  0.3851732909679413\n",
      "time taken :  0.5010662078857422\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "Epoch 19/19\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "train phase\n",
      "Iteration :  1  /  97\n",
      "loss :  1.5247031450271606\n",
      "lossl :  1.5795230865478516e-06 loss1 :  0.26386773586273193 loss2 :  0.33847299218177795 loss3 :  0.4412069320678711\n",
      "loss4 :  0.4811538755893707\n",
      "Iteration :  61  /  97\n",
      "loss :  1.4551243782043457\n",
      "lossl :  4.520826041698456e-05 loss1 :  0.24257434904575348 loss2 :  0.35557615756988525 loss3 :  0.37339550256729126\n",
      "loss4 :  0.48353320360183716\n",
      "time taken :  117.2787401676178\n",
      "--------------------------------------------------------------------------------------------------------------\n",
      "evaluate phase\n",
      "Iteration :  1  /  2\n",
      "loss :  2.1573545932769775\n",
      "lossl :  0.00032132118940353394 loss1 :  0.9960741400718689 loss2 :  0.5655725598335266 loss3 :  0.25903111696243286\n",
      "loss4 :  0.3363553583621979\n",
      "time taken :  0.4983394145965576\n",
      "--------------------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(num_epochs):  # loop over the dataset multiple times\n",
    "    \n",
    "    print('Epoch {}/{}'.format(epoch, num_epochs - 1))\n",
    "    print('-' * 110)\n",
    "    \n",
    "    # Each epoch has a training and validation phase\n",
    "    for phase in ['train', 'val']:\n",
    "        \n",
    "        if phase == 'train':\n",
    "            net.train(True)  # Set model to training mode\n",
    "            print(\"train phase\")\n",
    "            i = 0\n",
    "            rng_state = torch.get_rng_state()\n",
    "            new_idxs = torch.randperm(num_train)\n",
    "            c4_X = c4_data_tensor[new_idxs]\n",
    "            c4_Y = c4_target_tensor[new_idxs]\n",
    "            iter_per_epoch = iter_per_epoch_tr\n",
    "            print_every = print_every_tr\n",
    "        else:\n",
    "            net.train(False)  # Set model to evaluate mode\n",
    "            print(\"evaluate phase\")\n",
    "            i = 0\n",
    "            rng_state = torch.get_rng_state()\n",
    "            new_idxs = torch.randperm(num_valid)\n",
    "            c4_X = c4_data_tensor_val[new_idxs]\n",
    "            c4_Y = c4_target_tensor_val[new_idxs]\n",
    "            iter_per_epoch = iter_per_epoch_val\n",
    "            print_every = print_every_val\n",
    "\n",
    "        t1 = time.time()\n",
    "        for t in range(iter_per_epoch):\n",
    "            X_batch = c4_X[i: i+batch_size]\n",
    "            Y_batch = c4_Y[i: i+batch_size]\n",
    "            i += batch_size\n",
    "\n",
    "            Y_batch = Variable(Y_batch.cuda())\n",
    "            X_batch = Variable(X_batch.cuda())\n",
    "            #Y_batch = Variable(Y_batch)\n",
    "            #X_batch = Variable(X_batch)\n",
    "\n",
    "            optimizer.zero_grad()\n",
    "\n",
    "            outputs = net(X_batch)\n",
    "\n",
    "            lossl = objective(outputs[0], Y_batch[:, 0])\n",
    "            loss1 = objective(outputs[1], Y_batch[:, 1])\n",
    "            loss2 = objective(outputs[2], Y_batch[:, 2])\n",
    "            loss3 = objective(outputs[3], Y_batch[:, 3])\n",
    "            loss4 = objective(outputs[4], Y_batch[:, 4])\n",
    "            #loss5 = objective(outputs[5], Y_batch[:, 5])\n",
    "            final_loss = lossl + loss1 + loss2 + loss3 + loss4\n",
    "            \n",
    "            if phase == 'train':\n",
    "                final_loss.backward()\n",
    "                optimizer.step()\n",
    "                tr_loss_history.append(final_loss.data[0])\n",
    "                tr_epoch_losses[epoch].append(final_loss.data[0])\n",
    "            else:\n",
    "                val_loss_history.append(final_loss.data[0])\n",
    "                val_epoch_losses[epoch].append(final_loss.data[0])\n",
    "   \n",
    "            if (t % print_every == 0):\n",
    "                print('Iteration : ', t+1, ' / ', iter_per_epoch)\n",
    "                print('loss : ', final_loss.data[0])\n",
    "                print('lossl : ', lossl.data[0], 'loss1 : ', loss1.data[0], 'loss2 : ', loss2.data[0], 'loss3 : ', loss3.data[0])\n",
    "                print('loss4 : ', loss4.data[0])\n",
    "        t2 = time.time()\n",
    "        print(\"time taken : \", t2-t1)\n",
    "        print('-' * 110)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f = open(\"c5-c4.pkl\", \"bw\")\n",
    "torch.save(net.state_dict(), f)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f44717c29b0>]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8Vu51wJ+P9RpxrK297EjIdy4IAd+tLixiZvnAJGCxt+pr3p/Pj7b6MyvRNTtg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f44722380b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(tr_loss_history)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
